As global awareness of environmental protection has grown since the 1990s, eco-friendly vehicles have emerged as a critical focus in the automobile industry. The depletion of oil resources and rising fuel prices have further catalyzed interest in sustainable transportation options. This study aims to investigate how eco-friendly car characteristics (safety, price, technological advancement, and brand) and environmental factors (carbon neutrality and green ecological environment) influence consumers' perceived value, trust, and purchase intention. Using quantitative methodology, we conducted an online survey with questions adapted from previous research and analyzed data using SPSS 27.0 and Smart PLS 4.0. Our findings reveal that safety, price, and technological advancement significantly impact perceived value, while safety, price, and brand significantly affect trust. Regarding environmental factors, carbon neutrality influences perceived value, while both carbon neutrality and green ecological environment significantly impact trust. Furthermore, perceived value positively influences trust, and both significantly affect purchase intention. These results provide valuable insights for automobile manufacturers, policymakers, and marketers to better understand consumer preferences in the eco-friendly vehicle market, supporting the transition to sustainable transportation as consumers increasingly diversify their decision-making processes toward environmentally conscious choices.

YuanYuan Liu, Jun-Ho Huh, Member, KIICE. Journal of information and communication convergence engineering 2025;23:336-54. https://doi.org/10.56977/jicce.2025.23.4.336

Aquaculture industry is a major contributor to the world’s food supply and therefore provides food security globally. However, many problems exist for the development of the industry; they include poor disease control, lack of sufficient workers, and inefficient use of resources. The advent of big data and Artificial Intelligence (AI) technologies presents an opportunity for the aquaculture industry to utilize these technologies to improve operational practices in the entire industry. A proposed framework for a decision-making support system, utilizing a multi-source data hub will be established to enhance current operational practices in aquaculture. This framework will be designed to collect and integrate multiple types of big data from aquaculture (environmental data, trading data, and data from Internet sources), and then create a common platform for accessing and processing the collected data. Four main modules will be created: monitoring environmental factors for shrimp farming, sharing shrimp farming equipment, calculating costs/profits associated with shrimp farming, and predicting price changes. Experimental prototypes of web-based systems will be built and tested to evaluate their performance. Ultimately, the proposed framework will provide users with a user-friendly platform to access the analysis results and recommendations provided to support decisions made during shrimp aquaculture production. Therefore, our work will serve as a reference point for the adoption of leading-edge technologies into aquaculture production management and contribute to sustainable growth of the aquaculture industry.

Agricultural mechanization and advanced technology have developed significantly in the coffee industry. However, there are still requirements for human laborers to operate, monitor crop health care, and manage production. The integration of advanced technology can significantly enhance the production efficiency and management practices of agricultural enterprises. This paper aims to address these gaps by proposing and implementing a computer vision-based attendance tracking system on mobile platforms that are suitable for the requirements and limitations of agricultural enterprises. First, the face detection process involves interpreting and locating facial structure. Next, the model transforms a photographic image of a human face into digital data based on the unique features and facial structure. We utilize the InsightFace model with the buffalo_l variant, as well as ArcFace with a ResNet backbone, as a facial recognition algorithm. After capturing a facial image, the system conducts a matching process against the existing database to verify identity. Finally, we implement a mobile application prototype on both iOS and Android platforms, ensuring accessibility for farm workers. As a result, our system achieved 95.2% accuracy on the query set, with an average processing time of <200 ms per image (including face detection, embedding extraction, and database matching). The system performs real-time attendance monitoring, automatically recording the entry and exit times of farm workers using facial recognition technology, and enables quick registration of new workers. Our work is expected to enhance transparency and fairness in the human management process, focusing on the coffee farm use case.

Global urbanization and environmental transformations have modified the manner in which land is organized, used and governed

In recent years, renewable energy has become one of the most critical pillars for addressing the dual challenges of global energy shortages and environmental degradation